




"""simple docstring"""

def     _A					( __lowercase = 10							,     __lowercase = 22    ):





       """simple docstring"""



       lowerCamelCase__          =						range(1							,     a__    )
       lowerCamelCase__          =						range(1							,     a__    )
       return sum(
           1 for power in powers for base in bases if len(str(base**power    )    ) == power    )


if __name__ == "__main__":
  print(F'{solution(10, 22) = }')














"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"